Document Feature Extraction Based on Unoccupied Space Using Triangle Model: A Preliminary Work

Abstract

Document identification is used to extract information from a digital document such as Al-Quran, articles, agreement and so on. With increasing digital documents on the internet, it is important to identify that the document is genuine or not. There is existing research on document identification. However, the problem occurs when character recognition is done for particular language only and it is hard to recognize the character when the image dataset size was small. Therefore, the purpose of this research is to make use the similarities of each character language which is unoccupied space as the document feature extraction using triangle model. As for the preliminary work, the objective is to obtain a list of point selection that will be used for triangle model from the generated histogram. This research using an experimental design, the dataset was chosen is own dataset which document image will be used and IFN/ENIT dataset in order to handle the small size dataset. While the techniques involve is Otsu’s Model and histogram normalization. Experiments were conducted on own dataset word segment of documents. The results were able to obtain a list of point selection for both histograms vertical and horizontal. This tool is able to recognize document from other language documents.